Weekly Exercises #4

knitr::opts_chunk$set(echo = TRUE, error=FALSE, message=FALSE, warning=FALSE)

library(tidyverse)     # for data cleaning and plotting
library(lubridate)     # for date manipulation
library(openintro)     # for the abbr2state() function
library(palmerpenguins)# for Palmer penguin data
library(maps)          # for map data
library(ggmap)         # for mapping points on maps
library(gplots)        # for col2hex() function
library(RColorBrewer)  # for color palettes
library(sf)            # for working with spatial data
library(leaflet)       # for highly customizable mapping
library(carData)       # for Minneapolis police stops data
library(ggthemes)      # for more themes (including theme_map())

# Starbucks locations
Starbucks <- read_csv("https://www.macalester.edu/~ajohns24/Data/Starbucks.csv")

starbucks_us_by_state <- Starbucks %>% 
  filter(Country == "US") %>% 
  count(`State/Province`) %>% 
  mutate(state_name = str_to_lower(abbr2state(`State/Province`))) 

# Lisa's favorite St. Paul places - example for you to create your own data
favorite_stp_by_lisa <- tibble(
  place = c("Home", "Macalester College", "Adams Spanish Immersion", 
            "Spirit Gymnastics", "Bama & Bapa", "Now Bikes",
            "Dance Spectrum", "Pizza Luce", "Brunson's"),
  long = c(-93.1405743, -93.1712321, -93.1451796, 
           -93.1650563, -93.1542883, -93.1696608, 
           -93.1393172, -93.1524256, -93.0753863),
  lat = c(44.950576, 44.9378965, 44.9237914,
          44.9654609, 44.9295072, 44.9436813, 
          44.9399922, 44.9468848, 44.9700727)
  )

#COVID-19 data from the New York Times
covid19 <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")

Put your homework on GitHub!

If you were not able to get set up on GitHub last week, go here and get set up first. Then, do the following (if you get stuck on a step, don’t worry, I will help! You can always get started on the homework and we can figure out the GitHub piece later):

• Create a repository on GitHub, giving it a nice name so you know it is for the 4th weekly exercise assignment (follow the instructions in the document/video).
  
• Copy the repo name so you can clone it to your computer. In R Studio, go to file –> New project –> Version control –> Git and follow the instructions from the document/video.
  
• Download the code from this document and save it in the repository folder/project on your computer.
  
• In R Studio, you should then see the .Rmd file in the upper right corner in the Git tab (along with the .Rproj file and probably .gitignore).
  
• Check all the boxes of the files in the Git tab under Stage and choose commit.
  
• In the commit window, write a commit message, something like “Initial upload” would be appropriate, and commit the files.
  
• Either click the green up arrow in the commit window or close the commit window and click the green up arrow in the Git tab to push your changes to GitHub.
  
• Refresh your GitHub page (online) and make sure the new documents have been pushed out.
  
• Back in R Studio, knit the .Rmd file. When you do that, you should have two (as long as you didn’t make any changes to the .Rmd file, in which case you might have three) files show up in the Git tab - an .html file and an .md file. The .md file is something we haven’t seen before and is here because I included keep_md: TRUE in the YAML heading. The .md file is a markdown (NOT R Markdown) file that is an interim step to creating the html file. They are displayed fairly nicely in GitHub, so we want to keep it and look at it there. Click the boxes next to these two files, commit changes (remember to include a commit message), and push them (green up arrow).
  
• As you work through your homework, save and commit often, push changes occasionally (maybe after you feel finished with an exercise?), and go check to see what the .md file looks like on GitHub.
  
• If you have issues, let me know! This is new to many of you and may not be intuitive at first. But, I promise, you’ll get the hang of it!

Instructions

• Put your name at the top of the document.

• For ALL graphs, you should include appropriate labels.

• Feel free to change the default theme, which I currently have set to theme_minimal().

• Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!

• When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.

Warm-up exercises from tutorial

These exercises will reiterate what you learned in the “Mapping data with R” tutorial. If you haven’t gone through the tutorial yet, you should do that first.

Starbucks locations (ggmap)

1. Add the Starbucks locations to a world map. Add an aesthetic to the world map that sets the color of the points according to the ownership type. What, if anything, can you deduce from this visualization?

world<-
  get_stamenmap(bbox=c(left=-175, bottom=-75, right=175, top=75.5),
                maptype="terrain",
                zoom=2)
Starbucks2<-
Starbucks %>% 
  dplyr::rename_all(funs(make.names(.))) 
ggmap(world)+
  geom_point(data=Starbucks2,
             aes(x=Longitude, y=Latitude, color=Ownership.Type),
             alpha=0.5,
             size=0.75)+
  theme_map()+
  theme(legend.background = element_blank())

The vast majority of Starbucks locations around the world are either company-owned or licensed. Interestingly, however, in Japan and Korea the Starbucks are overwhelmingly joint ventures. Though I’m not entirely sure what that means, my guess is that Starbucks has an agreement with an East Asian coffee chain and they partner operate their stores. This probably gives Starbucks a big leg up in that part of the world, though I do wonder why that arrangement doesn’t seem common elsewhere. I was also surprised by the utter lack of Starbucks stores in Africa, most of Australia, and Brazil. Is it because of a lack of demand in those regions? Government regulations? Poor return on investment? This also surprises me, because I would expect each of those regions to be fairly large coffee bean producers. Is there a reason Starbucks wouldn’t put its stores in the same region as its plantations? This map gives you lots of plot points to visualize the distribution of Starbucks stores, but not really any other information to explain that distribution.

2. Construct a new map of Starbucks locations in the Twin Cities metro area (approximately the 5 county metro area).

tc_metro<-
  get_stamenmap(bbox=c(left=-93.9, bottom=44.7, right=-92.6, top=45.2),
                maptype="terrain",
                zoom=9)

tc_starbucks<-
Starbucks2 %>% 
  filter(Country=="US",
         State.Province=="MN")
ggmap(tc_metro)+
  geom_point(data=tc_starbucks,
             aes(x=Longitude, y=Latitude),
             alpha=0.5,
             size=1.5, 
             color="blue")+
  theme_map()

3. In the Twin Cities plot, play with the zoom number. What does it do? (just describe what it does - don’t actually include more than one map).

The Zoom number changes the level of detail the map shows. The smaller the number, the more zoomed out (less detail) there is. At first I started with a zoom of 0.75, as I did with the world map. However, that is waaaaaaaay too zoomed out for a map of the metro area; it wasn’t even recognizable as a map! However, if I made the zoom too large of a number then everything began looking very pixelated, and took an extremely long time to load.

4. Try a couple different map types (see get_stamenmap() in help and look at maptype). Include a map with one of the other map types.

tc_metro<-
  get_stamenmap(bbox=c(left=-93.9, bottom=44.7, right=-92.6, top=45.2),
                maptype="toner-lite",
                zoom=9)

tc_starbucks<-
Starbucks2 %>% 
  filter(Country=="US",
         State.Province=="MN")
ggmap(tc_metro)+
  geom_point(data=tc_starbucks,
             aes(x=Longitude, y=Latitude),
             alpha=0.5,
             size=1.5, 
             color="blue")+
  theme_map()

5. Add a point to the map that indicates Macalester College and label it appropriately. There are many ways you can do think, but I think it’s easiest with the annotate() function (see ggplot2 cheatsheet).

tc_metro<-
  get_stamenmap(bbox=c(left=-93.9, bottom=44.7, right=-92.6, top=45.2),
                maptype="terrain",
                zoom=9)
ggmap(tc_metro)+
  geom_point(data=tc_starbucks,
             aes(x=Longitude, y=Latitude),
             alpha=0.5,
             size=1.5, 
             color="blue")+
  annotate("text", x=-93.1, y=44.938, label="Macalester College", color="orange", size=3)+
  theme_map()

Choropleth maps with Starbucks data (geom_map())

The example I showed in the tutorial did not account for population of each state in the map. In the code below, a new variable is created, starbucks_per_10000, that gives the number of Starbucks per 10,000 people. It is in the starbucks_with_2018_pop_est dataset.

census_pop_est_2018 <- read_csv("https://www.dropbox.com/s/6txwv3b4ng7pepe/us_census_2018_state_pop_est.csv?dl=1") %>% 
  separate(state, into = c("dot","state"), extra = "merge") %>% 
  select(-dot) %>% 
  mutate(state = str_to_lower(state))

starbucks_with_2018_pop_est <-
  starbucks_us_by_state %>% 
  left_join(census_pop_est_2018,
            by = c("state_name" = "state")) %>% 
  mutate(starbucks_per_10000 = (n/est_pop_2018)*10000)

6. dplyr review: Look through the code above and describe what each line of code does.

The first line prints/names a new dataset (called census_pop_est_2018) for the manipulations made to the csv file over the next couple lines. Next, the 2018 census estimate is read into the code, and the “state” column is split into separate columns for the period in front of each state name, and the state name itself. The new column with just the dots in it is then deselected, leaving us a variable with values that are just regular state names. Then, a new column is created with all of those same state names, but shifted to entirely lower case. This is so we can join it with the starbucks data (that dataframe has state names in lower case, and joining requires them to be exactly the same.)

The starbucks by state dataset is piped in and joined with our newly modified census dataset. They are joined by their mutual variable, and we tell R that the “state” variable in the census dataset is the same as “state_name” in the starbucks dataset. We joined it in such a way that all the cases from the starbucks set are retained, while we discard those from the census dataset that do not have a match in starbucks. Finally, we create a new column that takes the number of starbucks in each state (“n”) divided by the variable from the census dataset that tells us the 2018 population (est_pop_2018). By itself, this would give us the number of Starbucks per capita, but then it is multiplied by 10,000 to give us the number of Starbucks per 10,000 people in each state. We call the new dataset with this variable “starbucks_with_2018_pop_est”.

7. Create a choropleth map that shows the number of Starbucks per 10,000 people on a map of the US. Use a new fill color, add points for all Starbucks in the US (except Hawaii and Alaska), add an informative title for the plot, and include a caption that says who created the plot (you!). Make a conclusion about what you observe.

us_map <- map_data("state")

us_starbucks<-
  Starbucks2 %>% 
  filter(Country=="US")

us_starbucks_points<-
starbucks_with_2018_pop_est %>%
  left_join(us_starbucks,
            by=c("State/Province"="State.Province")) %>% 
  filter(!state_name%in%c("alaska", "hawaii"))

us_starbucks_points %>% 
  rename(region=state_name) %>% 
ggplot()+
  geom_map(map=us_map,
           aes(map_id=region,
               fill=starbucks_per_10000))+
  expand_limits(x=us_map$long, y=us_map$lat)+
  theme_map()+
  scale_fill_viridis_c(option="inferno")+
  geom_point(data=us_starbucks_points,
             aes(x=Longitude, y=Latitude),
             size=1)+
  theme(legend.background = element_blank())+
  labs(title="Starbucks per 10,000 People",
       caption="Katie Herrick")

A few of your favorite things (leaflet)

8. In this exercise, you are going to create a single map of some of your favorite places! The end result will be one map that satisfies the criteria below.

• Create a data set using the tibble() function that has 10-15 rows of your favorite places. The columns will be the name of the location, the latitude, the longitude, and a column that indicates if it is in your top 3 favorite locations or not. For an example of how to use tibble(), look at the favorite_stp_by_lisa I created in the data R code chunk at the beginning.

• Create a leaflet map that uses circles to indicate your favorite places. Label them with the name of the place. Choose the base map you like best. Color your 3 favorite places differently than the ones that are not in your top 3 (HINT: colorFactor()). Add a legend that explains what the colors mean.

• Connect all your locations together with a line in a meaningful way (you may need to order them differently in the original data).

• If there are other variables you want to add that could enhance your plot, do that now.

my_favorite_places<-
  tibble(place=c("Grammy and Grampa's", "Nana's Cabin", "Science Museum", "Bass Lake", "Interstate Park", "Cafe Astoria", "Minnehaha Falls", "The Strand", "Pick-a-Bagel", "The Wharf", "Hirshhorn Museum", "Meridian Park"),
         long=c(-117.1, -94.6, -93.09813, -92.7, -92.7, -93.10764, -93.21117, -73.99, -73.984, -77.02, -77.022, -77.036),
         lat=c(32.8, 46.2, 44.94278, 45.6, 45.4, 44.94136, 44.91621, 40.733, 40.765, 38.88, 38.89, 38.921),
         top3=c(FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE))

color <- colorNumeric(palette=c("cornflowerblue", "darkgoldenrod1"), 
                     domain = my_favorite_places$top3)

leaflet(my_favorite_places) %>% 
  addTiles() %>% 
  addCircles(label = ~place,
             color = ~color(top3)) %>% 
  addLegend("topleft", colors="darkgoldenrod1", values = ~top3, bins=1, labels="Top 3 Favorite Places") %>% 
  addPolylines(lng=c(-117.1, -94.6, -93.09813, -92.7, -92.7, -93.10764, -93.21117, -73.99, -73.984, -77.02, -77.022, -77.036),
               lat=c(32.8, 46.2, 44.94278, 45.6, 45.4, 44.94136, 44.91621, 40.733, 40.765, 38.88, 38.89, 38.921),
               color="gray", weight=2)

As I was compiling my list of 12 favorite places, I realized they really span the whole country! Zoom in on the Twin Cities region, New York, and Washington, DC to view all the places more clearly. The points are connected in the chronological order in which I first visited them, starting with my grandparents’ house in San Diego.

Revisiting old datasets

This section will revisit some datasets we have used previously and bring in a mapping component.

Bicycle-Use Patterns

The data come from Washington, DC and cover the last quarter of 2014.

Two data tables are available:

• Trips contains records of individual rentals
• Stations gives the locations of the bike rental stations

Here is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}. This code reads in the large dataset right away.

data_site <- 
  "https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds" 
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")

9. Use the latitude and longitude variables in Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. This time, plot the points on top of a map. Use any of the mapping tools you’d like.

trips_and_stations<-
Trips %>% 
  group_by(sstation) %>% 
  summarize(n_departures = n()) %>% 
  arrange(desc(n_departures)) %>% 
  left_join(Stations, 
            by = c("sstation"="name"))

color <- colorBin(palette="Spectral", 
                     domain = trips_and_stations$n_departures,
                  bins=6)

leaflet(trips_and_stations) %>% 
  addTiles() %>% 
  addCircles(label = ~sstation,
             color = ~color(n_departures),
             opacity=1) %>% 
  addLegend("topleft", values = ~n_departures, pal=color, bins=6, title="Number of Station Departures")

10. Only 14.4% of the trips in our data are carried out by casual users. Create a plot that shows which area(s) have stations with a much higher percentage of departures by casual users. What patterns do you notice? Also plot this on top of a map. I think it will be more clear what the patterns are.

bike_clients<-
Trips %>% 
  mutate(casual=client=="Casual") %>% 
  group_by(sstation) %>% 
  summarize(n_casual=sum(casual==1),
            n_registered=sum(casual==0),
            prop_casual=n_casual/sum(n_casual, n_registered)) %>%
  arrange(desc(prop_casual)) %>% 
  left_join(Stations, 
            by = c("sstation"="name"))

color <- colorBin(palette="Spectral", 
                     domain = bike_clients$prop_casual,
                  bins=6)

leaflet(bike_clients) %>% 
  addTiles() %>% 
  addCircles(label = ~sstation,
             color = ~color(prop_casual),
             opacity=1) %>% 
  addLegend("topleft", values = ~prop_casual, pal=color, bins=6, title="Proportion of Casual Bikeshare Users")

The patterns of registered vs casual bicyclists is much clearer now that we have a map and familiar landmarks to look at! There are a few stations with unusually high rates of casual use: one is right by the Lincoln Memorial, one near the Martin Luther King, Jr. memorial at the Tidal Basin, one near the Capitol building, and a fourth near several of the Smithsonians. All of these sites are extremely popular with tourists, suggesting that many of the bikes at these stations are used by visitors to the city who want to go sight-seeing.

COVID-19 data

The following exercises will use the COVID-19 data from the NYT.

11. Create a map that colors the states by the most recent cumulative number of COVID-19 cases (remember, these data report cumulative numbers so you don’t need to compute that). Describe what you see. What is the problem with this map?

us_map<-
map_data("state")

covid19_2<-
covid19 %>%
  mutate(region=tolower(state)) %>% 
  filter(date=="2021-02-13")

covid19_2 %>% 
  ggplot() +
  geom_map(map = us_map,
           aes(map_id = region,
               fill = cases)) +
  expand_limits(x = us_map$long, y = us_map$lat) +
  theme_map()

The biggest issue with this map is that it does not take state populations into account. Therefore, the viewer’s attention is drawn disproportionately to heavily populated states such as California and Texas, which naturally have a larger absolute number of COVID cases simply due to the fact that they have a larger number of people. States such as South Dakota or Wyoming will likely be misrepresented; they have had large COVID outbreaks, but they don’t register because of their smaller populations. A better visualization of the pandemic would look at the cases per capita, or some other proportion like that.

12. Now add the population of each state to the dataset and color the states by most recent cumulative cases/10,000 people. See the code for doing this with the Starbucks data. You will need to make some modifications.

covid19_and_pop<-
  covid19_2 %>% 
  left_join(census_pop_est_2018,
            by=c("region"="state"))

covid19_and_pop %>%
  rename(population=est_pop_2018) %>% 
  ggplot() +
  geom_map(map = us_map,
           aes(map_id = region,
               fill = cases/population*10000)) +
  expand_limits(x = us_map$long, y = us_map$lat) +
  theme_map()

13. CHALLENGE Choose 4 dates spread over the time period of the data and create the same map as in exercise 12 for each of the dates. Display the four graphs together using faceting. What do you notice?

covid19_3<-
covid19 %>%
  mutate(region=tolower(state)) %>%
  filter(date=="2021-02-13"|
         date=="2020-10-01"|
         date=="2020-07-01"|
         date=="2020-03-15")

covid19_and_pop2<-
  covid19_3 %>% 
  left_join(census_pop_est_2018,
            by=c("region"="state"))

covid19_and_pop2 %>%
  rename(population=est_pop_2018) %>% 
  ggplot() +
  geom_map(map = us_map,
           aes(map_id = region,
               fill = cases/population*10000)) +
  expand_limits(x = us_map$long, y = us_map$lat) +
  theme_map()+
  theme(legend.position = "bottom")+
  facet_wrap(vars(date), scales="free")

I didn’t realize how much the pandemic really advanced in the beginning of this year. I remember feeling like the situation was dire back in March/April, but compared to now the virus was positively under control. This graph also shows that the far Northwest and Northeast are doing much better in their COVID management than the rest of the nation; I wonder if there are best practices that could be shared.

Katie’s Notes: Is there a way to change the number of bins in the color gradient? It’s almost impossible to see any variation between per capita cases in the first two dates I chose, but I couldn’t figure out how to fix that.

Minneapolis police stops

These exercises use the datasets MplsStops and MplsDemo from the carData library. Search for them in Help to find out more information.

14. Use the MplsStops dataset to find out how many stops there were for each neighborhood and the proportion of stops that were for a suspicious vehicle or person. Sort the results from most to least number of stops. Save this as a dataset called mpls_suspicious and display the table.

mpls_suspicious<-
MplsStops %>% 
  group_by(neighborhood) %>% 
  mutate(suspicious=problem=="suspicious") %>% 
  summarize(n_stops=n(),
            n_suspicious=sum(suspicious==1),
            prop_suspicious=n_suspicious/n_stops) %>% 
  arrange(desc(n_stops))

mpls_suspicious2<-
  mpls_suspicious %>% 
  left_join(MplsStops,
            by="neighborhood")

15. Use a leaflet map and the MplsStops dataset to display each of the stops on a map as a small point. Color the points differently depending on whether they were for suspicious vehicle/person or a traffic stop (the problem variable). HINTS: use addCircleMarkers, set stroke = FAlSE, use colorFactor() to create a palette.

pal <- colorFactor(palette=c("cornflowerblue", "darkgoldenrod1"),
                    domain=MplsStops$problem)

leaflet(MplsStops) %>% 
  addTiles() %>% 
  addCircles(lng = ~long,
             lat = ~lat,
             radius = 3,
             color = ~pal(problem),
             stroke=FALSE, 
             label= ~problem) %>% 
  addLegend("topleft", colors="cornflowerblue", values = ~problem, bins=1, labels="Suspicious Stops")

16. Save the folder from moodle called Minneapolis_Neighborhoods into your project/repository folder for this assignment. Make sure the folder is called Minneapolis_Neighborhoods. Use the code below to read in the data and make sure to delete the eval=FALSE. Although it looks like it only links to the .sph file, you need the entire folder of files to create the mpls_nbhd data set. These data contain information about the geometries of the Minneapolis neighborhoods. Using the mpls_nbhd dataset as the base file, join the mpls_suspicious and MplsDemo datasets to it by neighborhood (careful, they are named different things in the different files). Call this new dataset mpls_all.

mpls_nbhd <- st_read("Minneapolis_Neighborhoods/Minneapolis_Neighborhoods.shp", quiet = TRUE)

mpls_nbhd2<-
mpls_nbhd %>% 
  left_join(mpls_suspicious2,
            by=c("BDNAME"="neighborhood"))

mpls_all <- 
  mpls_nbhd2 %>% 
  left_join(MplsDemo,
            by=c("BDNAME"="neighborhood"))

17. Use leaflet to create a map from the mpls_all data that colors the neighborhoods by prop_suspicious. Display the neighborhood name as you scroll over it. Describe what you observe in the map.

color <- colorBin(palette="Spectral", 
                     domain = mpls_all$prop_suspicious,
                  bins=6)

leaflet(mpls_all) %>% 
  addTiles() %>% 
  addCircles(lng = ~long,
             lat = ~lat,
             radius = 3,
             stroke=FALSE, 
             label = ~BDNAME,
             color = ~color(prop_suspicious),
             opacity=1) %>% 
  addLegend("topleft", values = ~prop_suspicious, pal=color, bins=6, title="Proportion of 'Suspicious' Police Stops")

Unsurprisingly, the neighborhoods with the most “suspicious” police stops are also those that are lower-income and have larger populations of color. These include downtown Minneapolis, Powderhorn Park, Corcoran, Seward, Phillips, and Camden. Conversely, the neighborhoods with the lowest proportion of “suspicious” stops are higher-income, higher educated, and whiter. For example, the Nicollet Island area is nearly 80% white, over 70% of residents have a college degree, and the median income is more than $80,000; the proportion of suspicious police stops there was just over 17%. This sort of profiling has created a huge lack of trust between culturally diverse neighborhoods and the city, ultimately making everybody less safe and sowing seeds of prejudice.

18. Use leaflet to create a map of your own choosing. Come up with a question you want to try to answer and use the map to help answer that question. Describe what your map shows.

Question: Are people of color disproportionately targeted by traffic stops in Minneapolis?

traffic_stops<-
MplsStops %>% 
  filter(race!="NA",
         problem=="traffic") %>% 
  mutate(POC_stop=race!="White") %>%
  group_by(neighborhood) %>%
  summarize(n_stops=n(),
            n_POC_stops=sum(POC_stop==1), 
            prop_POC_stops=n_POC_stops/n_stops)

Mpls_traffic_stops<-
mpls_all %>% 
  left_join(traffic_stops,
            by=c("BDNAME"="neighborhood")) %>% 
  mutate(prop_neighborhood_POC=1-white)


pal <- colorBin(palette=c("Spectral"),
                    domain=Mpls_traffic_stops$prop_POC_stops,
                   bins=6)

leaflet(Mpls_traffic_stops) %>% 
  addTiles() %>% 
  addCircles(lng = ~long,
             lat = ~lat,
             radius = 3,
             stroke=FALSE, 
             label = ~prop_neighborhood_POC,
             color = ~pal(prop_POC_stops),
             opacity=1) %>% 
  addLegend("topleft", values = ~prop_POC_stops, pal=color, bins=6, title="Proportion of Traffic Stops Targeting People of Color")

This map is colored by the proportion of Minneapolis traffic stops that pulled over a person of color. You can scroll over an area of a map to see what percentage of the population are people of color. Unfortunately and unsurprisingly, people of color seem to be extremely disproportionately targeted by traffic stops. In many places, the percentage of traffic stops they account for is around 20% higher than their percentage of the population. In fact, some areas of North Minneapolis are approximately 34-40% POC, but they account for more than 90% of the traffic stops.

Katie’s Notes: Is there any way to add multiple variables to a label? I really wanted to include the neighborhoods in the label as well, but couldn’t figure out how to show it alongside the proportion of POCs. That’s not a huge deal if you’re familiar with the geography/demographics of Minneapolis, but would definitely make the map more accessible.

GitHub link

19. Below, provide a link to your GitHub page with this set of Weekly Exercises. Specifically, if the name of the file is 04_exercises.Rmd, provide a link to the 04_exercises.md file, which is the one that will be most readable on GitHub.

DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?